NASA Contractor Report 201678 ///_r cg/ The ASAC Air Carrier Investment Model (Second Generation) Earl R. Wingrove III and Jesse P. Johnson Logistics Management Institute, McLean, Virginia Robin G. Sickles Rice University, Houston, Texas David H. Good Indiana University, Bloomington, Indiana Contract NAS2-14361 April 1997 National Aeronautics and Space Administration Langley Research Center Hampton, Virginia 23681-0001 https://ntrs.nasa.gov/search.jsp?R=19970018389 2020-03-12T23:18:01+00:00Z
Transcript
NASA Contractor Report 201678
///_r cg/
The ASAC Air Carrier Investment Model
(Second Generation)
Earl R. Wingrove III and Jesse P. Johnson
Logistics Management Institute, McLean, Virginia
Robin G. Sickles
Rice University, Houston, Texas
David H. Good
Indiana University, Bloomington, Indiana
Contract NAS2-14361
April 1997
National Aeronautics and
Space AdministrationLangley Research CenterHampton, Virginia 23681-0001
To meet its objective of assisting the U.S. aviation industry with the technological
challenges of the future, NASA must identify research areas that have the greatest
potential for improving the operation of the air transportation system. Therefore,
NASA seeks to develop the ability to evaluate the potential impact of various ad-
vanced technologies. By thoroughly understanding the economic impact of ad-
vanced aviation technologies and by evaluating how these new technologies
would be used within the integrated aviation system, NASA aims to balance its
aeronautical research program and help speed the introduction of high-leverage
technologies. To meet these objectives, NASA is building an Aviation System
Analysis Capability (ASAC).
NASA envisions the ASAC primarily as a process for understanding and evaluat-
ing the impact of advanced aviation technologies on the U.S. economy. ASAC
consists of a diverse collection of models, data bases, analysts, and other indi-
viduals from the public and private sectors brought together to work on issues of
common interest to organizations within the aviation community. ASAC also will
be a resource available to the aviation community to perform analyses; provide
information; and assist scientists, engineers, analysts, and program managers in
their daily work.
The ASAC differs from previous NASA modeling efforts in that the economic
behavior of buyers and sellers in the air transportation and aviation industries is
central to its conception. To link the economics of flight with the technology of
flight, ASAC requires a parametrically based model with extensions that link air-
line operations and investments in aircraft with aircraft characteristics. This
model also must provide a mechanism for incorporating air travel demand and
profitability factors into the airlines' investment decisions. Finally, the model
must be flexible and capable of being incorporated into a wide-ranging suite ofeconomic and technical models that are envisioned for ASAC.
We describe a second-generation Air Carrier Investment Model that meets these
requirements. The enhanced model incorporates econometric results from the
supplyanddemand curves faced by U.S.-scheduled passenger air carriers. It uses
detailed information about their fleets in 1995 to make predictions about future
aircraft purchases. It enables analysts with the ability to project revenue passen-
ger-miles flown, airline industry employment, airline operating profit margins,
numbers and types of aircraft in the fleet, and changes in aircraft manufacturing
employment under various user-defined scenarios.
2
The ASAC Air Carrier Investment Model (Second Generation)
INTRODUCTION
NASA's Role in Promoting Aviation Technology
The United States has long been the world's leader in aviation technology for civil
and military aircraft. During the past several decades, U.S. firms have trans-
formed this position of technological leadership into a thriving industry with large
domestic and international sales of aircraft and related products.
Despite its historic record of success, the difficult business environment of the
recent past has stimulated concerns about whether the U.S. aeronautics industry
will maintain its worldwide leadership position. Increased competition, both
technological and financial, from European and other non-U.S, aircraft manufac-
turers has reduced the global market share of U.S. producers of large civil trans-
port aircraft and cut the number of U.S. airframe manufacturers to only two.
The primary role of the National Aeronautics and Space Administration (NASA)
in supporting civil aviation is to develop technologies that improve the overall
performance of the integrated air transportation system, making air travel safer
and more efficient, while contributing to the economic welfare of the United
States. NASA conducts much of the basic and early applied research that creates
the advanced technology introduced into the air transportation system. Through
its technology research program, NASA aims to maintain and improve the leader-
ship role in aviation technology and air transportation held by the United States
for the past half century.
The principal NASA program supporting subsonic transportation is the Advanced
Subsonic Technology (AST) program. In cooperation with the Federal Aviation
Administration and the U.S. aeronautics industry, the goal of the AST program is
to develop high-payoff technologies that support the development of a safe, envi-
ronmentally acceptable, and highly productive global air transportation system.
NASA measures the long-term success of its AST program by how well it con-
tributes to an increased market share for U.S. civil aircraft and aircraft component
producers and to the increased effectiveness and capacity of the national air trans-
portation system.
NASA' s Research Objective
To meet its objective of assisting the U.S. aviation industry with the technological
challenges of the future, NASA must identify research areas that have the greatest
potential for improving the operation of the air transportation system. Therefore,
3
NASA seeks to develop the ability to evaluate the potential impact of various ad-
vanced technologies. By thoroughly understanding the economic impact of ad-
vanced aviation technologies and by evaluating how those new technologies
would be used within the integrated aviation system, NASA aims to balance its
aeronautical research program and help speed the introduction of high-leverage
technologies. To meet these objectives, NASA is building an Aviation System
Analysis Capability (ASAC).
Goal of the ASAC Project: Identifying and Evaluating
Promising Technologies
The principal goal of ASAC is to develop credible evaluations of the economic
and technological impact of advanced aviation technologies on the integrated
aviation system. These evaluations would then be used to assist NASA program
managers to select the most beneficial mix of technologies for NASA to invest in,
both in broad areas, such as propulsion or navigation systems, and in more spe-
cific projects within the broader categories. Generally, engineering analyses of
this kind require multidisciplinary expertise, possibly using several models of dif-
ferent components and technologies, giving consideration to multiple alternativesand outcomes.
Airline Economics and Investment Behavior Drive the ASAC
The ASAC differs from previous NASA modeling efforts in that the economic
behavior of buyers and sellers in the air transportation and aviation industries is
central to its conception. To link the economics of flight with the technology of
flight, ASAC requires a parametrically based model that links airline operationsand investments in aircraft with aircraft characteristics. That model also must
provide a mechanism for incorporating air travel demand and profitability factors
into the airlines' investment decisions. Finally, the model must be flexible and
capable of being incorporated into a wide-ranging suite of economic and technical
models that are envisioned for ASAC. The remainder of this report describes a
second-generation Air Carrier Investment Model, developed by LMI, that meets
these requirements.
4
The ASAC Air Carrier Investment Model (Second Generation)
ECONOMIC AND STATISTICAL DERIVATION OF THE
BASIC ASAC AIR CARRIER INVESTMENT MODEL
Introduction
In creating the ASAC Air Carrier Investment Model (ACIM), we had some spe-
cific goals in mind. A primary objective was to generate high-level estimates
from broad industry-wide supply and demand factors. We envisioned being able
to forecast the demand for air travel under a variety of user-defined scenarios.
From these air travel demand forecasts, we then could estimate the derived de-
mand for the factors of production, the most important being the number of air-
craft in the fleets of U.S. passenger air carriers. We could also gauge the financial
health of the airline industry as expressed in its operating profit margins.
To create the model, we first identified 85 key U.S. airports from which flights
originate; then we developed airport-level demand models for passenger service
provided by major air carriers. Furthermore, we linked the air carrier-specific
demand schedules to an analysis of the carriers' technologies via their cost func-
tions expressed in terms of the prices of the major inputs--labor, fuel, materials,
and flight equipment. Flight equipment was modeled in an especially detailed
way by incorporating some key operating characteristics of aircraft.1
From the cost functions, we generated derived demand schedules for the factors of
production, in particular aircraft fleets. The derived demand schedules are func-
tions of the price of the factor of production, prices of other factors, parameters
that describe the aircraft and the network used by a carrier, and the level of pas-
senger service supplied.
Because it is so capital-intensive, the airline industry must earn an operating profit
margin of between 4 and 6 percent if it is going to maintain and expand its aircraft
fleet. Accordingly, we added an operating profit margin constraint to the model.
When this option is activated, passenger fare yields are adjusted up or down to
ensure that the target operating profit margins are met.
lActing under subcontract to LMI, Professor Robin Sickles of Rice University and ProfessorDavid Good of Indiana University generated the data sets and performed an econometric study ofmajor U.S. passenger airlines. They were assisted by Anthony Postert, a Ph.D. student at RiceUniversity. See the bibliography for a listing of previously published studies by Sickles andGood.
Overview of the Basic Air Carrier Investment Model
As shown in Figure 1, the basic Air Carrier Investment Model starts with the fac-
tors affecting the demand for scheduled passenger air travel at the airline and air-
port levels. It then examines the determinants of airline cost functions and the
resulting industry supply curve. The objective of both analyses is to obtain
parametric estimates for the air travel demand and airline cost functions. These
parametric estimates can then be combined with user-specified values of key sup-
ply and demand variables to generate industry-level forecasts of revenue passen-
ger-miles (RPMs) flown, 2 airline employment, number of aircraft in the fleet, and
operating margins under various scenarios.
Figure 1. Schematic of the Basic Air Carrier Investment Model
1. Estimate airline
and airport-level demand
iOwn fare yield 1
Competitor fare yield lPer capita income •
Population II
Unemployment rate
2. Estimate airline cost functions
Airline outputs i
Input quantifies m
Input prices
Stage length
Load factor
Seats per aircraft I IAircraft age 1 [
Percentage jets m [
Percentage wide-bodied aircraft_ I
Reve2u2jass2;j;rm_ r_les i .
Aircraft in fleet EB
Operating margins
5. Outputs from scenarios
Fare yield
Income growth
Population growth
Unemployment rate
Labor costs
Energy costs
Materials costs
Capital costs
Average stage lengthsLoad factor
Average seats per aircraft
Average age of aircraft
Percentage jets
Percentage wide-bodied aircraft
Demand
Supply
, r
Travel demand
Airline cost functions
3. Parametric estimates
4. Scenario variables
2One revenue passenger (person receiving air transportation from the air carrier for which re-
muneration is received by the air cartier) transported one statute mile.
6
The ASAC Air Carrier Investment Model (Second Generation)
Air Travel Demand
Our first analytical task was to develop a model of demand for an airline's pas-
senger service. From a particular airport at origin i, carrierj will generate a cer-
tain level of passenger traffic. The U.S. Department of Transportation's (DOT' s)
Origin and Destination data record a sample of all tickets; from these, the RPM
service originating at a particular airport for a particular carrier was constructed.
Demand for a carrier's service is driven by the carrier's passenger fare yield
(measured by the average ticket price for flights originating at airport i divided by
the average number of RPMs flown), its competitors' yields, and the size and eco-
nomic prosperity of the market. We modeled the economic characteristics of the
Standard Metropolitan Statistical Area (SMSA) surrounding the 85 airports in the
study in terms of the area's population, per capita income, and unemployment
rate. The period under consideration was from the first calendar quarter of 1979
through the last calendar quarter of 1992.
The demand function, in equation form, is
qt, i,j = Dt, i,j(Pt, i,j'Pt,i,c,Xt, i ), [Eq. 1]
where qt, i,j is the scheduled demand (in RPMs) originating at time t from airport i
for carrier j; Pt, i,j is the average yield for service originating at time t from airport
i for carrier j; and Pt, i,c is the average yield for the other carriers generating traffic
at time t from airport i. The xt, i are the other demand characteristics at time t for
airport i. Conventional treatments for firm and airport fixed effects were used.
These effects capture those important characteristics of a particular city that are
not easily measured, such as tourism effects. We used a log-log specification for
Equation 1, so that the regression coefficients may be interpreted as elasticities.
Total demand for an air carrier's passenger service was then constructed by sum-
ming the airport-specific demand equations. In terms of Equation 1, the total de-
mand for a carrier's service is given by
ap
= Y_ qt,i, j [Eq. 21qt,j i= 1
where ap is the number of airports (85).
Table1showsthedemandvariablesthatwereincorporatedinto themodel. All of
the explanatory variables were found to be statistically significant at the 95 per-cent level of confidence. 3
Table 1. Demand Variables
Variable Name Coefficient T-ratio
Own fares
Competitors' fares
Per capita income
Population
Unemployment rate
LNAVEOWN
LNAVEOTHER
LNPCI
LNPOP
LNUNRATE
-1.165
0.095
1.334
1.228
-0.121
-46.00
1.83
8.33
10.64
-4.63
Air Travel
Note: Estimates of firm and airport variables are not reported.
Supply
The second major component of our econometric study explains total carrier costs
in terms of output quantities, factor prices, aircraft attributes, and network traits. 4
The cost analysis was based mainly on observations from the Department of
Transportation (DOT) Form 41 data (discussed in more detail in Appendix A).
The cost data follow 17 U.S. passenger air carriers with quarterly observations
between the beginning of 1970 and the end of 1994. These firms were the largest
U.S. air carriers (or their descendents) that were operating at the time of deregula-
tion. This provides nearly total coverage of scheduled air traffic in 1970, to more
than 85 percent of the scheduled passenger air traffic by 1994. From the DOT
Form 41 data, we generated a separate set of demand equations for each of the
carrier's factors of production based on standard economic assumptions concern-
ing the cost-minimizing behavior of a carrier. In turn, these demand equations
permit examinations of the impact of factor price and factor productivity changes,
fleet and network configurations, and aircraft operating characteristics.
3The partial regression coefficients show the effects of changes in the independent variables(e.g., own fares, and competitors' fares) on the dependent variable (i.e., total demand for an aircarrier's passenger service). The T-ratios show the degree to which the partial regression coeffi-cients are statistically different from zero. For degrees of freedom over 30, a T-ratio of 1.96 pro-vides 95 percent confidence that the partial regression coefficient is not zero.
4Because of some double-counting of labor costs, the supply coefficients published in Win-grove et al., 1996, were wrong and had to be reestimated. Additional years were also included inthe data set. The revised values are shown in this report.
The ASAC Air Carrier Investment Model (Second Generation)
Scheduled RPM traffic for carrierj at time t was constructed as the sum of origi-
nating traffic supplied by the cartier for all airports from which it offered flights.
This was the first of the two outputs considered in the cost function below. The
second was the level of nonscheduled RPM service. The two generic output cate-
gories at time t for carrierj are designated Yt,j,1 and Yt,j,2 for scheduled and non-
scheduled RPM demand, respectively. The factors of production are labor,
energy, materials, and capital. Factor prices are labeled w. In the model, capital
refers to aircraft fleets only. Capital other than aircraft, such as ground structures
and ground equipment, is included in the materials category. Omitting the time
and firm subscripts, the transcendental logarithmic (translog) cost function is
given by
2 2 2
lnC=ao+Zi=lailnYi+Z Z aijlnyilnyj+i<_j j=l
Zi41fli tnwi + Z_<_qZ_=lflpq lnwp lnwq +
Zi41Pi aircraft attributes i In Wcapita I "+ _i2=1 _t_ network traits i
[Eq. 3]
Cost shares for labor, energy, and materials are given by
4
M/= fl;+ _,,flij lnwj [Eq. 4]j=l
The cost share for capital is
4 4
Mcapital = _capital + Z _capital,j In w j + Z P J aircraft attributes jj=l j=l
[Eq. 5]
The translog cost equation can be viewed roughly as a second-order approxima-
tion of the cost function dual to a generic production function. Symmetry and lin-
ear homogeneity in input prices are imposed on the cost function by therestrictions
........................................................................................................................................................................................................................................................................... eA CAir rr!e ves e t (Sec°n
mand shocks. Many transportation studies also interpret it as a proxy for service
quality. As load factors increase and the network becomes less resilient, the num-
ber and length of passenger flight delays generally increase as do the number of
lost bags and ticketed passengers who are bumped. Inflight service levels also
decline since the number of flight attendants is not generally adjusted upward as
the passenger load factor increases.
Estimates of the long-run cost function and summary statistics for various elas-
ticities are provided in Table 2.
Table 2. Supply Variables
Variable Name Coefficient T-ratio
Labor price
Labor price squared
Labor x energyLabor x materials
Labor x capital
Energy price
Energy price squared
Energy x materials
Energy x capital
Materials price
Materials price squared
Materials × capital
Capital price
Capital price squaredScheduled demand
Scheduled demand squaredNonscheduled demand
Nonscheduled demand squared
Scheduled x nonscheduled demand
Stage lengthLoad factor
Average seats
Average age
Percentage jets a
Percentage wide-bodied aircraft a
LNLP
LNLpA2
LNLPEP
LNLPMP
LNLPKP
LNEP
LNEpA2
LNEPMP
LNEPKP
LNMP
LNMpA2
LNMPKP
LNKP
LNKP^2
LNSQ
LNSQ^2
LNNQ
LNNQA2
LNSQNQ
LNSL
LNLF
XLNAS
XLNAA
XXPJ
XXPWB
0.376
-0.017
-0.011
0.047
-0.019
0.206
0.119
-0.106
- 0.002
0.297
0.099
- 0.039
0.121
0.060
0.844
- 0.090
0.098
- 0.122
0.150
-0.216
-0.818
0.027
- 0.009
0.002
- 0.020
N/A
- 1.06
- 2.43
3.60
- 2.84
N/A
35.63
- 30.60
-0.91
N/A
7.91
- 6.73
N/A
12.13
62.62
- 2.88
7.99
- 2.76
3.96
- 9.54
- 20.85
5.08
- 1.59
1.61
- 12.36
Note: Estimates of firm and quarterly dummy variables are not reported.
aAII other variables are expressed as natural logarithms.
11
USING THE MODEL
General Approach
The joint model of supply and demand for commercial passenger air service speci-
fied in our study and the inferences about factor demands that are imbedded in our
econometric results enable us to simulate the effects of emerging technologies.
We can also forecast the growth in total system demand for passenger service and
for factor inputs such as the number of aircraft in the fleet.
We follow several general steps when evaluating scenarios: First, we predict the
change in RPMs on the basis of economic forecasts and the demand equation es-
timates. Next, we estimate airline revenues on the basis of forecast RPM growth
and hypothesized changes in ticket prices. Then, we estimate airline operating
costs on the basis of forecasted RPM growth, changes in input prices, and changes
in aircraft and network characteristics. We predict the aircraft inventory from air-
line operating costs, the capital share equation, and hypothesized changes in air-
craft price and aircraft size. We follow a similar procedure to estimate airline
employment. Finally, we compare forecasts from the second-generation ASAC
Air Carrier Investment Model with predicted changes in RPMs, aircraft fleet, and
operating margins from other published forecasts.
Forecasting Changes in Travel Demand, Airline Costs,
and Aircraft Fleets
TRAVEL DEMAND
To predict changes in travel demand, the model starts with actual airline output
for calendar year 1995 and changes it over time based on the estimated demand
function coefficients and predicted changes in the explanatory variables. The
equation for predicting annual changes in demand is
5
%ARPM = _fl_ %A X, [Eq. 6]i=1
where the _i are the coefficients estimated from the econometric model and the X i
are the explanatory variables. Due to the logarithmic structure of the statistical
model, the coefficients are interpreted as elasticities. For example, the coefficient
of 1.334 on per capita income means that a 1 percent increase in per capita income
raises the demand for air travel by 1.334 percent.
12
The ASAC Air Carrier Investment Model (Second Generation)
The annual percentage change in per capita income, population, and unemploy-
ment are parameters entered by the user. The baseline model uses estimates of
population growth published by the Bureau of Labor Statistics. Per capita income
growth is not directly input into the model. Instead, the user provides estimates of
the long-run annual growth rates in gross domestic product and population. The
model then calculates the annual change in per capita income and uses it to gener-ate the demand forecast.
Fare variables are treated in one of two possible ways. User-defined rates of
change in fare yields can be input directly into the model, and their effects will be
estimated immediately. The second mode of operation, as described later in the
report, enables the user to set a series of profit rate constraints for each of the four,
5-year intervals in the forecast period. The user then instructs the model to vary
the fare yields until the profit rate constraints are met.
The econometric estimates of the demand function are based on quarterly traffic
volume for each airline and airport in the sample. While it is possible to build the
demand forecasts up from this highly detailed level, it would be time-consuming
and probably add more inaccuracy to the final estimate. Instead, we use the actual
RPM data for the domestic and international routes of U.S. scheduled passenger
airlines as the starting point, and grow demand at the rate indicated by Equation 6.
This imposes the constraint that output grows at the same rate for each airline.
While obviously inaccurate, this is not a significant bias in the model since our
goal at this time is to forecast industry-wide demand, costs, employment, and air-
craft fleets. For long-run forecasts such as those generated by the model, it is im-
material whether the aggregate demand for air travel is satisfied by a particularcarrier such as United Airlines or Delta Airlines.
For purposes of forecasting fares and for calculating industry travel demand, the
own-fare and other-fare changes are assumed to be identical. Therefore, the over-
all price effect is the sum of the two coefficients. The net effect shows that air
passenger travel is sensitive to price changes, but not unusually so. The model
predicts that a 10 percent reduction in fares will increase RPMs by 10.7 percent.
This implies that after holding other factors constant--such as population and in-
come---changes in air fares will have virtually no effect on total revenues col-
lected by the industry.
AIRLINE COSTS
Equation 3 describes the airline cost equation estimated for the model. As shown,
total costs are a function of airline outputs, factor costs, and aircraft and airline
network attributes. Using the supply parameter estimates shown in Table 2,
%ATC= _a i %A Yi Jr ___O_ij %A Yi %A yj -t- _._fli %A w i +i=l i<_j i=1
4 4 4
___ _ _pq % A W p %A W q .-_- ___ _o i %A aircraft attributes i % A Waircra fl [Eq. 7]p<_q q=l i=l
2
+ ___ %Anetwork traits ii=1
where %A means annual percentage change in the variable.
In Equation 7,factor costs, aircraft attributes, and network traits are user-defined
variables in the basic ASAC Air Carrier Investment Model. For labor and capital,
changes in factor costs are the net of price and productivity effects. Scheduled
and nonscheduled output changes are estimated directly in the demand model
forecasting component and then input into the cost functions. Therefore, changes
in output cannot be made directly by the user.
As with the demand forecasts, total costs are projected forward from the baseline
defined by the reported data. The model increases the costs at the rates predicted
by the model, given output forecasts, factor cost changes, and changes in aircraftand network characteristics.
AIRCRAFT FLEETS
Estimating the aircraft fleet required to meet the forecasted travel demand is a
somewhat more involved process. Four factors enter into the forecast of aircraftfleets:
• the changes in total airline costs,
• the estimated share of aircraft costs in total costs,
• the forecasted change in average aircraft price, and
• the forecasted change in average aircraft size.
Changes in total airline costs were discussed in the previous section. Referring to
Equation 5, the aircraft share of total costs is a function of factor costs and aircraft
14
The ASAC Air Carrier Investment Model (Second Generation)....................................................................................................................................................................................... .............................................................................................................................................................................................................................................................................................................................................................................
attributes. As with the cost and demand forecasts, we update the capital share
equation through the forecast period as a function of the rates of change in the
factor cost and aircraft attribute parameters. The equation for changes in the
capital cost share is
4
A Aircraft Cost Share = --[3_ircraft + Y_1 "fl_ircraft' j % k w_ji=
4
+ j _= 1pj % A aircraft attributesj
[Eq. 8]
The resulting capital share time-series predicts the fraction of total costs that will
be spent on aircraft investments. By multiplying this share estimate by total costs,
we obtain a time-series of capital investments in aircraft.
The final pieces of information needed to calculate the number of planes in the
aircraft fleet are the predicted levels of average aircraft price and average aircraft
size. The rate of growth in aircraft size is measured by the average number of
seats. The product of average aircraft price (holding size constant) and average
size are divided into the aircraft investment to get the estimated number of planes
in each airline's fleet. In equation form, the formula isr
number of aircraft =(capital share x total cos t)
(aircraft price × average size)[Eq. 9]
The required fleets for all the airlines are then summed to get the industry esti-mate.
FACTOR PRODUCTIVITIES
Once time-series have been generated for RPMs, number of airline workers, and
number of planes in the fleet, it is possible to estimate factor productivities for
labor and capital. In the baseline scenario, labor productivity increases from 1.25
million RPMs per worker in 1995 to 1.47 million RPMs per worker in 2015.
Similarly, capital productivity increases from 132 million RPMs per plane in 1995
to 184 million RPMs per plane in 2015. We make use of these year-by-year base-
line factor productivities when alternative scenarios are evaluated. Specifically,
except where NASA technologies explicitly impact them, we assume that al-
though other changes in supply and demand variables will impact the airlines'
cost equations, factor productivities will not change.
15
ENHANCEMENTS TO THE BASIC MODEL
Converting Technical Impacts into Economic Effects
In the second generation ACIM, we model the impacts of NASA technologies in
the following manner: We first assume that NASA technologies begin to enter
the fleet in 2005 and all new aircraft purchased during the period 2006 to 2015
will incorporate the new technology. Additionally, we assume that 5 percent of
the existing fleet will be replaced or upgraded annually to take advantage of the
new technology. If travel demand grows at a compound annual rate of 5 percent
during the period 2005 to 2015 and all the other assumptions hold, approximately
69.3 percent of the RPMs flown in 2015 will be in aircraft that incorporate the
new technology. This figure defines the baseline penetration rate for the new
technology and can be varied by the user.
Translating the technical impacts of the new technology into economic effects is
similarly straightforward. The first step is to estimate the gross impact of the
technology in terms of eight functional cost categories. These categories are:
flight personnel costs, aircraft fuel, maintenance costs, other variable operating
Calculate seat sizes I Calculate acquisition costs
of the new Stage 3 aircraft "-------_] of the new Stage 3 aircraft
purchased to meet RPM growth I purchased to meet RPM growth
Calculate future til..__i Calculate total ]i_..f Calculate total number _
market shares ' market value of of Stage 3 aircraftStage 3 aircraft added to the fleet
Calculate U.S. sales Ito U.S. manufacturers I .I
Calculate deltas: Ii Number of aircraft
Market value
U.S. market value
U.S. manufacturing employment
The static track performs the replacement analysis of the current fleet. This analy-
sis is static in the sense that replacement purchases are somewhat unresponsive to
the introduction of new technology. This unresponsiveness is a function of the
huge capital costs of acquiring an aircraft as well as financial losses associated
with prematurely retiring an aircraft. Consequently, the introduction of new tech-
nology into the existing fleet occurs primarily because new aircraft are used as
replacements for retired aircraft. New technology only marginally affects the ac-
tual retirement schedule in that some premature retirements will occur among air-
craft that are already near the end of their useful lives.
The static analysis consists of three steps: estimation of the retirement schedule of
the current fleet, adjustments to that schedule due to noise regulations, and calcu-
lation of the replacement costs for retired aircraft.
The dynamic analysis performs an analysis of the additional aircraft purchased to
meet future RPM growth. An estimate of the number of additional aircraft pur-
chased in any given year is an output of the basic ACIM. The dynamic analysis
decomposes that aggregate number into a distribution of additional aircraft pur-
19
chased per seat-size category. Then the acquisition costs of those aircraft are es-timated.
The total number of new aircraft purchased, as well as their total market value, is
then found by summing the results of the static and dynamic tracks. Market share
data are used to project the portion of sales to U.S. owned carriers by U.S. air-
frame manufacturers. Finally, employment effects are estimated.
As a last step, differences in aircraft produced, their corresponding market values,
the U.S. portion of those sales, and resulting employment levels may be compared
across scenarios. Details of the step-by-step analysis are shown in Appendix C.
SCENARIOS AND FORECASTS
Operating Profit Margins and Fare Yields
An early version of our model predicted increasing profitability for the airline in-
dustry during the forecast years. This was clearly unreasonable for the highly
competitive airline industry. To make the model reflect actual industry conditions
more faithfully, three important characteristics of the industry were incorporatedinto the model:
• competition among airlines that keeps operating profits at realistic levels,
• links between airline costs and fare yields, and
• interdependency between fares and profitability.
Our model accommodates these features with a straightforward extension. It
builds an industry-wide target profit rate into the model. To meet the target profit
rate, the model adjusts fare yields until the target is met. This approach incorpo-
rates the impact of competition into the forecast and enables the degree of compe-
tition to be set directly through the target margins. By choosing an appropriate
profit rate, the user can also ensure that adequate capital is available to finance the
purchase or lease of the aircraft needed to satisfy the growing demand for airtravel.
As implemented in the model, separate target profit rates can be set for each of the
four, 5-year intervals within the forecast period. Specifying four distinct periods
permits the user to include changes in the economic environment during the fore-
cast period. For example, many financial analysts today claim that airlines will
20
The ASAC Air Carrier Investment Model (Second Generation)
not purchase additional aircraft until their balance sheets are "repaired." One way
to implement this concept is to set a higher profit margin during the first 5-year
interval and then reset the target at a lower, historically reasonable level. Such a
scenario will keep fares and profits at a higher level for 5 years, while reducing
the derived demand for aircraft and other inputs.
The model does not impose the margin constraint in every single year. Instead,
the model iterates changes in fare yield until the target margin in the final year of
each interval is satisfied. Since the model uses a constant rate of fare change
within each 5-year interval, the operating margin does not equal the target until
the final year of the period. In practice, the profit margin moves in equal incre-
ments within the interval. If the target margins are the same at the beginning and
end of the 5-year interval, the margin will be the same in each year.
This approach explicitly lets fare changes be set by the degree of competition and
the level of costs throughout the industry. It allows for a market-based mecha-
nism for translating cost changes into profits and fare changes. One implication
of this approach is that cost-reducing technologies will primarily benefit the trav-
eling public and not result in higher profits for the airlines over the long run.
While some airlines may benefit for a short while, competition will eventually
drive fares down as most airlines adopt the cost-reducing technology.
This analysis is consistent with economic theory and also appears to be an accu-
rate description of the airline industry. The relatively low profit margins reported
by the airline industry demonstrate the speed with which innovations and new
technologies diffuse throughout the industry. The ease of entry for new airlines
with access to cheap older aircraft keeps profit margins low, and it is unlikely that
this situation will change in the near future.
Several alternative profit measures could be used to implement this approach in
our model. We chose to use the operating profit margin, which is revenues minus
operating costs, divided by revenues. The operating margin does not reflect inter-
est paid on debts or a return to common shareholders, both important elements of
cost in a capital-intensive industry such as the airlines. Capital expenses vary sig-
nificantly from airline to airline, and in particular, will be strongly affected bywhether the airline flies new or old aircraft.
An equally important question is what target operating margin should be used in
the model. Boeing states that an operating profit margin of about 5 percent is
probably required for the airline industry to remain healthy enough to meet in-
creasing travel demands and purchase new aircraft. An examination of the his-
torical data tends to confirm this conclusion. Figure 3 shows operating margins
21
and the percentage change in aircraft fleets for nine major air carriers (American,
Continental, Delta, Eastern, Northwest, Trans World, United, USAir, and South-
west) from 1978 through 1993. While there is clearly a great amount of variabil-
ity in the year-to-year numbers, the years of greatest and most consistent growth in
fleets was the mid-1980s. This was also the only extended period of profitability
for the industry during these years. While the change in aircraft fleets may be
somewhat skewed because of the effect of mergers over this time, the numbers
clearly demonstrate a strong correlation between profitability and aircraft invento-ries. The results are reinforced when one considers that new aircraft deliveries in
the early 1990s were frequently from orders placed much earlier. The chart dem-
onstrates clearly the importance of incorporating a limit on airline profits in theinvestment model.
Figure 3. Operating Profit Margins and Aircraft Fleet Growth
for Nine Major Airlines
Percent
30
25
20
15
10
5
0
-5
-10
/..... -_ r 1....... -_ ........ r.......... _.............1....... T...........1..... -r ..........T...... t r' "l......... -1
78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93
Year + Change in fleet
....._---.. Operating margin
Baseline Scenario
Using the baseline values specified in Appendix D for the supply and demand
variables, the second-generation ASAC Air Carder Investment Model projects
annual growth in travel demand of 4.56 percent for the period of 1995 through
2005. This prediction compares quite favorably with annual growth forecasts of
22
The ASAC Air Carrier Investment Model (Second Generation)
4.74 percent and 4.36 percent from the Boeing Company (Boeing) and the Fed-
eral Aviation Administration (FAA), respectively. In terms of the number of air-
craft required to satisfy this growth in travel demand, the second-generation
ACIM projects annual growth in the U.S. scheduled passenger airline fleet of
2.63 percent for the period of 1995 through 2005. This prediction is lower than
Boeing's forecast of a 3.20 percent annual growth and the FAA's forecast of a
3.05 percent annual growth. The 121 to 170 seat class is projected to have the
greatest number of aircraft, while the 171 to 240 seat class is expected to experi-
ence the largest growth in percentage terms. Other details for the baseline sce-
nario are found in Appendix D.
Other Scenarios: Comparisons
To demonstrate the reasonableness and utility of our model, we evaluated a set of
alternative scenarios that correspond to the effects that various NASA AST pro-
gram elements might have. These are summarized in Table 4. Details of the
technology evaluations and illustrative printouts from the ASAC Air Carrier In-
vestment Model are in Appendix E.
Table 4. Baseline and Other Scenario Forecasts
Technology
Baseline
A
B
C
Gross changes foraffected variables
(%)
N/A
A/C fuel = -5
A/C fuel = -14
A/C price = +2
Flight crew = -4A/C fuel = -4Maintenance = -4
A/C productivity = +4
Compound annualrates of changein travel demand
(2005-2015)(%)
4.17
4.23
4.31
4.31
Compoundannual rates of
change inairline
employment(2005-2015)
(%)
3.42
3.47
3.56
3.41
Compoundannual rates
of change inaircraft fleet
(2005-2015)(%)
2.53
2.58
2.67
2.38
CONCLUSIONS
To link the economics of flight with the technology of flight, NASA' s ASAC re-
quires a parametrically based model that links airline operations and investments
in aircraft with aircraft characteristics. That model also must provide a mecha-
23
nism for incorporating air travel demand and profitability factors into the airlines'
investment decisions. Finally, the model must be flexible and capable of being
incorporated into a wide-ranging suite of economic and technical models that are
envisioned for ASAC.
The second-generation Air Carder Investment Model meets all of these require-
ments. The enhanced model incorporates econometric results from the supply and
demand curves faced by U.S. scheduled passenger air carriers. It uses detailed
information about their fleets in 1995 to make predictions about future aircraft
purchases. It provides analysts with the ability to project revenue passenger-miles
flown, airline industry employment, airline operating profit margins, number and
types of aircraft in the fleet, and changes in aircraft manufacturing employment
under various user-defined scenarios. Future work will extend the analysis to
other regions of the world, most notably Europe and Asia.
24
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Appendix A
Airline Production Data Description
INTRODUCTION
The airline production data set includes four inputs: labor; energy; flight capital;
and a residual category called materials that includes supplies, outside services,
and nonflight capital. The data set also includes two outputs: scheduled and non-
scheduled revenue passenger-miles--and two network traits: stage length and
load factor. Flight capital is described by four aircraft attributes: the average size
(measured in seats); the average age; and the separate proportions of aircraft in the
fleet that are jet-powered or wide-bodied designs.
Our most comprehensive data set includes information for the 17 largest U.S. air
carriers that were operating at the time of deregulation or their descendant airlines.
The carriers included are American, Braniff International, Continental, Delta,
Eastern, Frontier, North Central, Northwest, Ozark, Piedmont, Republic, South-
ern, Texas International, Trans World, United, USAir, and Western. This provides
nearly total coverage of scheduled air traffic in 1970, the beginning of the data, to
more than 85 percent of the scheduled passenger air traffic by 1994, the data set's
end. This information is quarterly, air carrier-specific information and results in
1,137 total observations. Attention was restricted to the traditional certificated
carriers because routine data reporting was well-established for them at the time
of deregulation. New entrants can be added to our data set with some difficulty.
However, it should be remembered that these carriers have little experience in
providing the often burdensome reporting required by Department of Transporta-
tion (DOT) Form 41 and that noncompliance results in virtually no sanctions.
Consequently, new entrant data tends to be of significantly lower quality. The
version of the data described in more detail below provides the largest, cleanest
data available on the production of U.S.-scheduled passenger air transport.
The procedure used in constructing the data set has changed considerably over the
last decade. As more and more data sources become available, it will change
further. One of the most significant factors in these changes has been an adapta-
tion to the changes in the reporting requirements of DOT Form 41. In order to
maintain comparability over time, data from all versions of Form 41 must be
A-1
LABOR
mapped into a single version. The latest significant revision, which occurred in
1987, eliminated many of the specific functional accounts that were used previ-
ously. The most significant changes occurred in the areas of labor, supplies, andoutside services. This latest version of Form 41 data is the most restrictive in that
it provides the least detail in most cases. In other instances, the 1985 revision of
Form 41 data is somewhat more restrictive. However, many of these changes
were in place for only a short period of time. Where the 1985 restrictions were
most severe, 1987-equivalent accounts were estimated. This occurred most seri-
ously in the area of ground-based capital, where lease payments and capitalized
leases had to be allocated between flight and ground capital. In other cases, it
seemed reasonable to estimate 1985 accounts from the 1987 data provided. The
objective was to maintain as much detail as possible in all areas of air carrier pro-duction.
The construction of the individual input and output categories is described in the
next several sections. In cases where price and quantity pairs for a specific input
or output are constructed, several subcomponents to that input or output are first
constructed. Then these are aggregated into a single input or output using a mul-
tilateral Tornqvist-Theil index number procedure. 1 The result of this procedure is
a price index (much like the consumer price index) that aggregates price informa-
tion for commodities having disparate physical units. When total expenditures of
the input or output category are divided by this price index, an implicit quantity
index is produced.
Labor, energy, materials, flight capital, and output are discussed in the sectionsbelow.
The labor input was composed of 93 separate labor accounts aggregated into five
major employment classes (flight deck crew, flight attendants, mechanics, passen-
ger/cargo/aircraft handlers, and other personnel). This is shown in Table A-1.
We do not attempt to correct for differing utilization rates since we do not have
information on the number of hours worked by the labor inputs. Expenditures in
nue, profit, or transformation functions. In this case, the transcendental logarithmic (translog) costfunction is underlying; expenditure shares are used to weight each subcomponent' s contribution tothe overall index number. For a detailed explanation, refer to Diewert (1976); Caves, Christensen,and Diewert (1982); and Good, Nadiri, and Sickles (1992) in the Bibliography.
A-2
Airline Production Data Description
these five subcomponents are constructed from the expenditure data in DOT
Form 41 Schedules P5, P6, P7, and P8.
Following the 1987 modification in Form 41, Schedules P7 and P8 were dramati-
cally simplified, eliminating many separate expense accounts. "Mechanics" and
"handlers" appear as lines 5 and 6 of the new Schedule P6. In order to be more
compatible with the new Schedule 6, trainees and instructors were moved into the
"other personnel" category. "Flight attendant" expense was calculated by sub-
tracting accounts 5123 and 5124 from Schedule P5 from line 4 ("total flight per-
sonnel") on the new Schedule P6.
Other labor-related expenses--such as personnel expenses, insurance, pension,
and payroll taxes--were included as labor expenses. The labor-related expenses,
accounts, and schedules from which they were obtained are listed in Table A-2.
Consumer prices:electric, gas (89%),and sanitary service(11%)
Producer prices:processed foods
Consumer prices:air fares
GNP deflator fornonresidential fixedinvestment
Communications
Insurance
Outside servicesand aimra_ mainte-nance
Supplies
Utilities
Passenger food
Commissions
Ground equipment,rented
(See note below)
6144
Jorgensen-Halluser price
Landing fees percapacity-tonlanded
Ground equipment,owned
Landingfees
Note: Total e_ )enditures associated with ground equipment and structures were calculated using aperpetual inventory method with a 1958 benchmark, assuming a 2O-yearexpected life, straight-line de-preciation, and interest rates assuming a Moody's BAA bond rating. The tax advantages, including in-vestment tax credits (along with the special transition rules under the 1986 tax revisions) relevant at thetime were also incorporated into the carrier's expenditure on ground capital owned. As with the laborindex, a multilateral Tornqvist-Theil index number procedure was used to generate price quantity combi-nations for each carrier at each quarter over the 23-year span of the data.
A-7
OUTPUT
Our data set provides several measures of airline output and its associated char-
acteristics. The most commonly used measure of carrier output is the revenue
ton-mile. Our data set provides this measure as well as measures of revenue out-
put that are disaggregated into scheduled and nonscheduled output. Nonscheduled
output includes cargo and charter operations. We further provide measures of air-
line capacity. This again can be disaggregated into scheduled and nonscheduled
operations. Revenue and traffic data were available from DOT Form 41. These
data enabled us to construct price and quantity figures for seven different outputs
produced by the typical airline. These different services and the accounts from
which the revenue data were obtained are given in Table A-6. Again, the price
per unit (passenger-mile or ton-mile) of the relevant service was constructed by
dividing the revenue generated in the category by the physical amount of output in
that category. These prices were normalized to 1.0 in the baseline period (the
third quarter of 1972).
In cases where a carrier offered only one type of service (the convention was to
call this "first class"), the service was redefined to be coach class. The reporting
of revenue and traffic in charter operations between cargo and passenger service
was very sporadic. These two outputs were combined into a single category with
passenger-miles converted to ton-miles, assuming an average weight of 200
pounds per passenger (including baggage). Changes in DOT Form 41 in 1985 led
to the elimination of the distinction between express cargo and air freight. Conse-
quently, these two categories also were collapsed.
Table A-6. Carrier Revenues and Output Quantities
P3T1
P3
T1
P3T1
P3T1
P3
T1
P3T1
P3T1
Schedule Accounts Type of service
3901.1K141
3901.2
K142
3905Z243+Z244+Z245
First class passenger revenueFirst class passenger-miles
Coach passenger revenue
Coach passenger-miles
Mail transportation revenueMail ton-miles
3906.1K246
3906.2
K247
3907.1V140
3907.2V246+V247
Express cargo revenueExpress cargo ton-miles
Air freight revenueAir freight ton-miles
Charter passenger revenueCharter passenger-miles
Charter cargo revenueCharter cargo ton-miles
A-8
Airline Production Data Description
Three different price and quantity index pairs are generated. The first is total
revenue-output and uses the multilateral Tomqvist-Theil index number procedure
on all of the revenue-output categories. The second uses the Tornqvist-Theil in-
dex number procedure on the two passenger categories. The third results from the
use of the index number procedure on mail, cargo, and charter services.
The capacity of flight operations is also provided in our data set. This describes
the total amount of traffic generated, regardless of whether or not it was sold.
While it is possible to distinguish between an unsold coach seat and an unsold
first-class seat (they are of different sizes), such distinctions are not logically pos-
sible in the case of cargo operations (mail and cargo could be carried in the same
location). Consequently, our measure of airline capacity includes only three broad
categories: first-class seat-miles flown, coach seat-miles flown, and nonscheduledton-miles flown. The accounts and schedules from Form 41 are shown in Ta-
ble A-7.
Table A-7. Capacity Measures
Schedule Accounts Type of service
T1 K321 First-class seat-miles
T1 K322 Coach seat-miles
T1 Z280 - (K321+K322)/10 Nonscheduled ton-miles
With the change to T100 as the primary data base for airline traffic in 1990, carri-
ers are no longer required to report available seat-miles, revenue seat-miles, or
revenues by the level of passenger service. Instead, these amounts are aggregated
with revenues supplied as account 3901 on Schedule P1 after 1990.
Again, the convention that a passenger along with baggage is 200 pounds (one-
tenth of a ton) is used to construct the nonscheduled ton-miles. Potential revenues
that could be collected, if all services were sold, are constructed assuming that the
prices for each of these categories remain the same as for output actually sold. In
other words, the price for first-class revenue passenger-miles flown is imputed to
first-class available seat-miles flown. Again, the Tomqvist-Theil index number
procedure is used to generate price and quantity pairs for total capacity output,
passenger capacity output, and nonscheduled capacity output.
Two important measures of the carder' s network are also generated. The first is a
passenger load factor. This is found by dividing revenue passenger-miles by
A-9
available seat-miles (i.e., [K141+K142]/[K321+K322]). This measure is gener-
ally related to flight frequency with a lower number indicating more frequent
flights and consequently a higher level of service. Other definitions of load factor
are possible, such as dividing the total passenger revenue collected
(3901.1+3901.2) by the total that would be collected were the planes flown full
(derived from the passenger capacity output times passenger capacity price). If
desired, these can easily be constructed using information in the data set. Stage
length also provides an important measure of cartier output. Generally, the
shorter the flight, the higher the proportion of ground services required per pas-
senger-mile and the more circuitous the flight (a higher proportion of aircraft
miles flown is needed to accommodate the needs of air traffic control). This gen-
erally results in a higher cost per mile for short frights than for longer flights. Av-
erage stage length is found by dividing total revenue aircraft miles flown (Z410)
by total revenue aircraft departures (Z510).
A-IO
Appendix B
Converting Technical Impacts into Economic Effects
The basic Air Carder Investment Model uses a supply function that incorporates
four factors of production: labor, energy, materials, and capital. To translate the
likely effects of NASA-developed technologies (which are usually thought of in
terms of reduced block times, less fuel burned, lower maintenance costs, etc.) into
appropriate reductions in the costs of these four factors of production, we had to
create a matrix of functional cost categories versus factors of production.
Because we were interested in fully accounting for airline operating costs, we used
Department of Transportation Schedule P-6 (Operating Expenses by Objective
Groupings). This report is only filed by Group II and III air carriers. See Ta-ble B-1 for the elements of the various lines of this schedule.
Table B-1. Lines of Schedule P-6
Line number Elements
3
4
5
6
7
10
11
12
16
17
18
19
22
23
24
25
26
27
28
30
31
32
33
34
35
Salaries and wages of general management personnel
Salaries and wages of flight personnel
Salaries and wages of maintenance personnel
Salaries and wages of aircraft and traffic-handling personnel
Salaries and wages of other airline personnel
Personnel expenses
Employee benefits and pensions
Payroll taxes
Aircraft fuel and oil (including fuel and oil taxes)
Maintenance materials
Passenger food
Other materials
Advertising and other promotion
Communications
Insurance
Outside flight equipment maintenance
Passenger traffic commissions
Cargo traffic commissions
Other services
Landing fees
Rentals
Depreciation
Amortization
Other
Transport-related expenses
B-1
While using Schedule P-6 creates some loss of precision because of aggregation,
it has the virtue of full visibility of all reported costs. The scheme we used to al-
locate the various lines of Schedule P-6 to the appropriate cells is shown in Ta-ble B-2.
Table B-2. Derivation of Matrix (Reference Lines From Schedule P-6)
Category Labor Energy
Flight personnel 4* --
Aircraft fuel 16
Maintenance 5* --
Other variable operating costs 6* --
Fixed operating costs (3+7)* --
Flight equipment
Other capital
* = plus an allocated share of lines 10, 11, and 12.
Materials Capital
17+25
18+26+27+30
19+22+23+24+28+34+35
-- 13.6% times flightequipment
13.6% timesground propertyand equipment
We collected Schedule P-6 data for 10 years (1980, 1982, 1983, 1985, 1987,
1988, 1990, 1992, 1993, and 1995) for 12 carriers (American, Braniff, Continen-
tal, Delta, Eastern, Northwest, Ozark, Piedmont, Republic, Trans World, United,
and USAir). The choice of years and airlines were made to be as consistent as
possible with the econometric study of airline costs performed by Sickles and
Good (described in the main bodyof the report).
The two cells labeled "flight equipment" and "other capital" deserve separate ex-
planation. We generated a time series for the estimated economic value of a car-
tier' s aircraft fleet as follows. A key driver was the number of aircraft days. We
divided this figure by 365.25 to derive the average number of aircraft in a carrier's
fleet for the year. This estimate therefore includes both owned and leased aircraft.
If the average number of aircraft increased from one year to the next, the differ-
ence was multiplied by the industry-wide average cost of new aircraft shipped in
that year. This figure represented the value of new aircraft in a carrier's fleet.
The value of old aircraft in a carder's fleet was depreciated by 3.33 percent per
year (which implicitly assumes an economically useful lifetime of 30 years). For
years in which the average number of aircraft decreased from one year to the next,
the depreciated value of the old aircraft was scaled by the ratio of the latest year' s
number of aircraft compared with the prior year' s number to account for retire-
ments. The time series for the value of ground property and equipment was pulled
B-2
Converting Technical Impacts into Economic Effects
directly from Form 41 balance sheet data (element 1649.0 without any accumu-
lated depreciation).
In any given year, the economic value of aircraft was multiplied by the sum of air
carriers' weighted average cost of capital (separately estimated at 10.3 percent)
plus depreciation of 3.3 percent to estimate the return to flight equipment capital.
Similarly, the value of ground property and equipment was also multiplied by 13.6
percent to estimate the return to other capital. Because we used this procedure to
separately estimate the cost of airline capital, no use was made of lines 31 to 33 of
the P-6 Schedule.
For the 10 years and 12 carriers, we collected the Schedule P-06 cost data and es-
timated the returns to capital as described above. The average cost shares are as
shown in Table B-3. In comparing our shares with those implicit in the ASAC
Air Cartier Investment Model, there is a high degree of agreement.
Table B-3. Mean Cost Shares
Labor EnergyCategory (%) (%)
Flight personnel 13.8 --Aircraft fuel -- 18.4
Maintenance 4.1 --
Other variable operating costs 11.6 --
Fixed operating costs 5.8 --
Flight equipment -- --
Other capital -- --
Totals 35.3 18.4
From supply variable estimates 37.6 20.6
Materials Capital(%) (%)
4.2
13.9
13.4
-- 12.7
2.0
33.5 12.7
29.7 12.1
Totals(%)
13.8
18.4
8.3
25.6
19.3
12.7
2.0
100.0
100.0
As shown in Table B-4, there was some variability in these cost shares. Energy
was particularly volatile, declining from a high share of 28.9 percent in 1980 to a
low share of 11.6 percent in 1995. The materials subcategory of other variable
operating costs had its low share of 9.5 percent in 1980 and its high share of
17.8 percent in 1993.
B-3
Table B-4. Standard Deviations of Cost Shares
Category
Flight personnelAircraft fuel
Maintenance
Other variable operating costs
Fixed operating costs
Flight equipment
Other capital
Totals
Labor(%)
0.8
0.2
0.8
0.3
1.4
Energy Materials Capital Totals(%) (%) (%) (%)
6.1 m
0.8
2.7
1.9
1.7
0.2
6.1 5.1 1.7 0.0
0.8
6.1
0.8
2.1
1.8
1.7
0.2
B-4
Appendix C
Derivation of the Air Carrier InvestmentModel Extensions
The goal of the ACIM Extensions is to translate the high-level estimates from the
basic ACIM into a finer level of detail. This appendix gives a detailed explana-tion of how this is done.
INPUT STRUCTURE
There are four sets of input streams needed to run the ACIM Extensions. They are
1. a subset of the output stream from the basic ACIM;
2. an aircraft inventory database that describes the 1995 fleet;
3. a matrix of market shares that was estimated from historic aircraft sales data;
and
4. a set of user-defined inputs that describe/specify a scenario.
Each of the four sets of inputs are described in a section below.
INPUT #1, ASAC OUTPUT STREAM
The basic ACIM generates supply and demand estimates in the form of time-
series for revenue passenger miles, airline employment, number of aircraft in the
fleet, and operating profit margins. A sample of the aircraft fleet time-series isshown in Table C. 1.
C-1
Table C-1. Aircraft Fleet Time-Series
Year Baseline number of aircraft
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
4,179
4,279
4,419
4,545
4,676
4,812
4,932
5,055
5,183
5,315
5,451
5,587
5,727
5,872
6,021
6,175
6,329
6,488
6,652
6,821
6,995
The time-series of number of aircraft in the fleet of the U.S. scheduled passenger
carriers can be used to estimate the number of new aircraft purchased in any year
to meet RPM growth; this is given by
aircraft purchased to meet RPM growtht = aircraft in fleett - aircraft in fleett_b
INPUT #2, AIRCRAFT INVENTORY
The DOT Schedule B-43 Airframe Inventory for 1995 was used to estimate the
initial distribution of aircraft by seat-size category and the expected retirementschedule of the fleet. The first 15 lines of B-43 data for American Airlines are
shown in Table C.2. LMI added noise stage data to the aircraft inventory data-base.
C-2
Owned/
lease/capital Carrier
lease code
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
CL AA
Table C-2. Extract from B-43 Data
A/C
manufacturer A/C type
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-727-2
BOE B-757
BOE B-757
Number of
Year of A/C type se_s as
Tail first Serial numeric specified
number delive_ number code bycarrier
N701AA 81 22459 715 150
N702AA 81 22460 715 150
N703AA 81 22461 715 150
N705AA 81 22462 715 150
N890AA 80 22006 715 150
N891AA 80 22007 715 150
N892AA 80 22008 715 150
N893AA 80 22009 715 150
N894AA 80 22010 715 150
N895AA 80 22011 715 150
N896AA 80 22012 715 150
N897AA 80 22013 715 150
N898AA 80 22014 715 150
N899AA 80 22015 715 150
N634AA 90 24592 622 194
N635AA 90 24593 622 194
INPUT #3, MARKET SHARE DATA
The estimation of market shares begins with historic jet airplane deliveries to U.S.
customers. This set of data contains the name of the manufacturer and type of
every jet delivered to U.S. customers from 1966 to 1995. The data first are split
into the eight seat-size categories. For each category, a regression is run to predict
the market share by firm. The finn-level market shares are then summed to pro-
duce market shares by manufacturing country. Then, a set of category-by-
category corrections are made. In the largest seat class (>350 seats) and the 171 to
240 seat class, only U.S.-based manufacturers have delivered these types of jets to
U.S. customers. Airbus has delivered similar size jets to non-U.S, customers and
it also has undelivered orders to U.S. customers. To correct the regression results
for these two categories, it is assumed that manufacturers take 10 years to attain a
first sale in a new market. After that, the firm gets an exponential growth rate
based on capturing 12 percent of the market after another 10 years. The regres-
sion results for the other seat-size categories were corrected by using an exponen-
tial smoothing algorithm that incorporates a time- and number-weighted moving
average of the previous 10 years' sales. This correction generates future market
share estimates that consistently lie between exponentially smoothed, continuous,
and bounded long-run market share estimates.
C-3
INPUT #4, USER-DEFINED INPUTS
The last set of inputs are user-defined variables that enable analysts to further de-
fine or refine a scenario, perform sensitivity analysis over a small subset of vari-
ables, or perform simple what-if types of analyses. The variables are initially set
to a baseline value, but users may enter alternative values. The user-defined in-
puts are easily classified into one of the following categories:
• Retirement age data
• Aircraft cost data
• Interest rate data
• Other data.
The retirement age data specify the ages at which aircraft are nominally retired.
Retirement age rules vary in two dimensions: the year in which the retirement
rules are changed and narrow-body versus wide-body aircraft.
The baseline aircraft cost figures were derived from the Boeing 1995 Current
Market Outlook. These data specify the acquisition costs of new aircraft by seat-
size category.
The interest rate data specify the real interest rate in terms of its two components,
the nominal interest rate (or airline cost of capital) and the rate of price increase.
The actual values of both components are subject to debate and a variety of values
can be used and justified. Therefore, it is advisable to determine the sensitivity of
any solution to these parameters.
The other data represent a set of varied single inputs. The year of noise law en-
forcement enables users to explicitly examine the effects of changing the year in
which Stage 2 aircraft may no longer operate in the United States. The figure for
aircraft shipments per airframe manufacturing worker allows for varying these
workers' productivity. The user-defined inputs are shown in Table C-3.
C-4
Derivation of the A CIM Extensions
Table C-3. User-Defined Inputs
Data description Baseline value
Entry in service year for incorporation of newer retirement rules forpassenger aircraft
Average age at which narrow body aircraft are retired prior toincorporation of new rules
Average age at which narrow body aircraft are retired afterincorporation of new rules
Average age at which wide body aircraft are retired prior toincorporation of new rules
Average age at which wide body aircraft are retired afterincorporation of new rules
Acquisition cost of a new aircraft by number of seats:
>350
241-350
171-240
121-170
91-120
70-90
50-69
Under 50
Nominal interest rate or airline cost of capital
Nominal rate of price increase
Year by which 100 percent of the fleet must be Stage 3
Aircraft shipments per airframe manufacturing worker
198O
25
28
28
31
$160,000,000
$116,700,000
$58,000,000
$44,000,000
$28,000,000
$22,000,000
$19,400,000
$14,800,000
10.3%
3.0%
2000
$122,700
STATIC ANALYSIS
The static analysis performs the replacement analysis of the current fleet. Starting
with the aircraft inventory database, an expected retirement year is assigned to
each aircraft by the user-defined age rules. The number of aircraft retired per year
is found by summing all the retirements that are expected to occur in a particular
year. This represents the minimal replacement schedule (new aircraft added to
replace those retiring due to old age).
The first retirement schedule estimated is for Stage 3 passenger aircraft. The sec-
ond retirement schedule estimated is for Stage 2 passenger aircraft. Stage 2 pas-
senger aircraft are further analyzed with respect to noise regulations. The Stage 3
noise law forces all Stage 2 aircraft from the fleet by its year of implementation.
A break-even calculation is performed to determine which of the Stage 2 planes
C-5
subject to early retirement should be hushkitted and which should be immediately
replaced. The Stage 2 passenger aircraft retirement schedule is then modified toinclude this effect.
These two retirement schedules are combined to generate the baseline retirement
schedule for the fleet. Our assumption is that aircraft retired due to old age are
replaced with Stage 3 aircraft of the same seat-size category. For each year, the
baseline retirement schedule of the fleet is multiplied by the acquisition cost of a
new passenger aircraft of that seat size.
Since this analysis is based on the 1995 year-end inventory, it will only need to be
redone if the following inputs are changed:
• Retirement ages or rules
• Year of the noise law incorporation
• Acquisition cost of an aircraft by seat size
• Either of the two components of the real interest rate.
DYNAMIC ANALYSIS
The dynamic analysis allocates the aircraft purchased to meet future RPM growth
over the eight seat-size categories by the following method. We first take the
time-series of the number of planes in the fleet from the basic ACIM and calculate
the yearly differences of this time series. These differences are the aircraft added
to meet RPM growth.
We use the growth rate in seats per aircraft from the user inputs to create a time
series starting with the average seats figure estimated from the aircraft inventorydatabase.
We estimate the average-seats figure for the new aircraft by the following for-mula:
(Average seats per aircraftt * Total aircraftt - (Average seats per aircraftt_l * Total
aircraftt_l * (1+ growth rate in the stage length))/Aircraft addedt
Fifty percent of the aircraft added to meet RPM growth are allocated to the seat-
size category in which the average-seats figure falls. The remaining aircraft added
to meet RPM growth are distributed across all eight seat-size categories according
C-6
to the 1995distributionschedule.Theresultingcompositionof thefleet is trackedat theseat-sizecategorylevel, yearby year.
NEW AIRCRAFT SUMMARY
Once both the number and costs of the replacement passenger aircraft and the new
growth passenger aircraft are known, they are summed to produce the total num-
ber of Stage 3 aircraft added to the fleet and its corresponding market value.
U.S. SALES TO U.S. AIR CARRIERS
The U.S. sales calculation is an estimate of the portion of new aircraft sales that
accrue to U.S. airframe manufacturers. For each year, the total market value of
the Stage 3 aircraft by seat-size category is multiplied by the corresponding U.S.
market share. When summed over a year, this gives the sales of U.S.-
manufactured aircraft to U.S. passenger air carriers in a particular year.
U.S. MANUFACTURING EMPLOYMENT
The additional U.S. airframe manufacturing employment resulting from the sales
of new Stage 3 aircraft is estimated in the summary section. The average sales per
worker is a user input. Work years of employment generated are found by divid-
ing the U.S. sales in each seat-size category by the average sales per airframe
manufacturing worker, summed across all seat classes.
SUMMARY CALCULATIONS
The summary calculations present the key data in terms of differences between the
baseline scenario and the user-defined scenario, which is usually characterized as
the introduction of a new technology. The relevant data are the differences acrossthe baseline and user-defined scenarios for
• the number, by seat-size category, of Stage 3 aircraft purchased;
• the total market value of Stage 3 aircraft purchased;
• the U.S. share of Stage 3 aircraft purchased;
• the market value of the U.S.-manufactured Stage 3 aircraft; and
Table D-I shows the default values for the annual changes (from 1995 through
2015) of the key variables in the ASAC Air Carrier Investment Model.
Table D-1. Default Values
Federal Aviation
Variable Boeing a (%) Administration b (%) LMI (%)
Change in fare yield
Income growth
Population growth
Change in unemployment rate
Laborprice change
Laborproductivity effect
Fuelcostchange
Materials cost change
Capital price change
Capital productivity effect
Change in stage length
Change in load factor
Change in average seats per aircraft
Change in average age of aircraft
Change in proportion of jet aircraft
Change in proportion of wide-bodiedaircraft
-1.10
2.40
0.00
1.60
-1.60
(reflects 0.9% in-
crease in fuel priceminus 2.5% increase
in fuel efficiency)
0.00
0.40
(reflects more miles
flown per year peraircraft)
0.30
0.60
-1.18
2.62
m
0.00
0.52
(reflects more ai_borne houB peryear
peraimraft)
0.38
0.10
0.80
0.002275
-1.07
2.51
0.94
0.00
0.00
1.60
-1.60
0.00
0.00
0.46
0.38
0.20
0.70
0.74
0.00
0.002275
Note: All economic values are measured in constant dollars. Therefore, the annual percentareal rates of change.
aThe Boeing figures are an amalgamation of forecasts from the 1993 through 1996 editions of the CurrentMarket Outlook. If forecasts from multiple years were available, preference was given to the latest edition.Additionally, preference was given to U.S.-specific forecasts; otherwise, worldwide forecasts were substituted.
b-l-he FAA figures were derived from FAA Aviation Forecasts: 1996-2007. The FAA focuses exclusively onU.S. carriers.
e changes are
D-1
FORECASTED VALUES
When the consensus figures are inserted into the ASAC Air Carrier Investment
Model, the values of future travel and aircraft requirements, shown in Table D-2,
are predicted for the period 1995 through 2005. These forecasts may be compared
with those from Boeing and the FAA.
Table D-2. Forecasted Values
Variable Boeing a FAA b LMI
Revenue passenger-mile (RPM) growth
Absolute RPMs (billions) in 2005
Growth in number of aircraft
Absolute number of aircraft in 2005 c
4.74%
888.5
3.20%
5,332
4.36%
834.1
3.05%
5,537
4.50%
855.6
2.69%
5,451
Note: The Boeing, FAA, and LIM figures for number of aircraft in the 1995 fleet were 3,890; 4,100; and4,179 respectively.
aThe Boeing figures are an amalgamation of forecasts from the 1993 through 1996 editions of the Cur-rent Market Outlook. If forecasts from multiple years were available, preference was given to the latestedition. Additionally, preference was given to U.S.-specific forecasts; otherwise, worldwide forecasts weresubstituted.
bThe FAA figures were derived from FAA Aviation Forecasts: 1996-2007. The FAA focuses exclusivelyon U.S. carriers.
CCargo aircraft are excluded.
Table D-3 shows the projected distribution of aircraft in 2005 by seat size cate-
gory.
Table D-3. Projected Distribution of Aircraft by Seat Size in 2005
Seat size Under 50
Number of 612aircraft
50-69 70-90 91-120
115 97 846
121-170 171-240 241-350 Over350
2,484 349 138810
Total
5,451
D-2
Appendix E
Details of Alternative Scenarios
TECHNOLOGY A
Technology A is hypothesized to reduce the weight of key components of the air-
frame. As a consequence, block fuel is reduced for the average flight by five per-
cent. Assuming a penetration rate of 69.3 percent by the year 2015, this
improvement is modeled as a 0.35 percent compound annual reduction in fuel
costs during the period 2006 to 2015. When the target operating margin con-
straints are binding at five percent, the reduced airline operating costs are passed
along to the traveling public as fare reductions. Consequently, an additional
34.1 billion revenue passenger miles are flown, airline employment increases by
nearly 24,000 work-years, and the number of aircraft in the fleet increases by 37.
The estimated total value of these aircraft is $1.9 billion (in 1995 dollars) and the
U.S. market share is projected at 76 percent. This is expected to generate
11,968 work-years of employment at U.S. airframe manufacturers.
E-1
Table E-1. RPM Growth for Technology A
Baseline Total RPM Revised Total RPM
Year (billions) (billions)
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
550.7
576.6
603.8
632.3
662.1
693.3
723.1
754.1
786.6
820.4
855.6
891.7
929.2
968.4
1,009.2
1,051.7
1,095.2
1,140.5
1,187.6
1,236.7
1,287.8
4.34%
550.7
576.6
603.8
632.3
662.1
693.3
723.1
754.1
786.6
820.4
855.6
892.2
930.3
970.0
1,011.5
1,054.7
1,098.8
1,144.8
1,192.7
1,242.6
1,294.6
4.37%
34.1
E-2
Details of Alternative Scenarios
Table E-2. Airline Employment for Technology A
Baseline Revised
Year Employment Employment
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
438,983
455,299
472,316
490,068
508,590
527,917
545,799
564,383
583,700
603,780
624,659
645,810
667,787
690,626
714,365
739,041
764,055
790,041
817,042
845,101
874,262
3.50%
438,983
455,299
472,316
490,068
508,590
527,917
545,799
564,383
583,700
603,780
624,659
646,173
668,539
691,794
715,975
741,124
766,580
793,036
820,537
849,126
878,852
3.53%
23,608
E-3
Table E-3. Fleet Size for Technology A
Baseline Number Revised Number
Year of Aircraft of Aircraft
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
4,179
4,297
4,419
4,545
4,676
4,812
4,932
5,055
5,183
5,315
5,451
5,587
5,727
5,872
6,021
6,175
6,329
6,488
6,652
6,821
6,995
2.61%
4,179
4,297
4,419
4,545
4,676
4,812
4,932
5,055
5,183
5,315
5,451
5,590
5,734
5,882
6,035
6,193
6,350
6,513
6,680
6,853
7,032
2.64%
37
E-4
Details of Alternative Scenarios
TECHNOLOGY B
Technology B is hypothesized as an improvement in jet propulsion technology.
As a consequence, block fuel is reduced for the average flight by 14 percent, but
the price of the airframe/engine combination increases by 2 percent. Assuming a
penetration rate of 69.3 percent by the year 2015, these changes are modeled as a
1.02 percent compound annual reduction in fuel costs and a 0.14 percent com-
pound annual increase in capital price during the period 2006 to 2015. When the
target operating margin constraints are binding at 5 percent, the reduced airline
operating costs are passed along to the traveling public as fare reductions. Conse-
quently, an additional 86.8 billion revenue passenger miles are flown, airline em-
ployment increases by over 60,000 work-years, and the number of aircraft in the
fleet increases by 93. The estimated total value of these aircraft is $4.9 billion (in
1995 dollars) and the U.S. market share is projected at 76 percent. This is ex-
pected to generate 30,231 work-years of employment at U.S. airframe manufac-
turers.
E-5
Table E-4. RPM Growth for Technology B
Baseline Total RPM Revised Total RPM
Year (billions) (billions)
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
550.7
576.6
603.8
632.3
662.1
693.3
723.1
754.1
786.6
820.4
855.6
891.7
929.2
968.4
1,009.2
1,051.7
1,095.2
1,140.5
1,187.6
1,236.7
1,287.8
4.34%
550.7
576.6
603.8
632.3
662.1
693.3
723.1
754.1
786.6
820.4
855.6
893.0
931.9
972.6
1,015.1
1,059.4
1,104.5
1,151.5
1,200.5
1,251.6
1,304.9
4.41%
86.8
E-6
Details of Alternative Scenarios
Table E-5. Airline Employment for Technology B
Baseline Revised
Year Employment Employment
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
438,983
455,299
472,316
490,068
508,590
527,917
545,799
564,383
583,700
603,780
624,659
645,810
667,787
690,626
714,365
739,041
764,055
790,041
817,042
845,101
874,262
3.50%
438,983
455,299
472,316
490,068
508,590
527,917
545,799
564,383
583,700
603,780
624,659
646,745
669,722
693,630
718,510
744,405
770,515
797,668
825,909
855,286
885,850
3.57%
60,109
E-7
Table E-6. Fleet Size for Technology B
Baseline Number Revised Number
Year of Aircraft of Aircraft
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
4,179
4,297
4,419
4,545
4,676
4,812
4,932
5,055
5,183
5,315
5,451
5,587
5,727
5,872
6,021
6,175
6,329
6,488
6,652
6,821
6,995
2.61%
4,179
4,297
4,419
4,545
4,676
4,812
4,932
5,055
5,183
5,315
5,451
5,595
5,744
5,898
6,056
6,220
6,383
6,551
6,724
6,903
7,088
2.68%
93
E-8
Details of Alternative Scenarios
TECHNOLOGY C
Technology C is hypothesized to reduce the block time for the average flight by
4 percent. Assuming a penetration rate of 69.3 percent by the year 2015, this im-
provement is modeled as 0.14 percent, 0.28 percent, and 0.03 percent compound
annual reductions in labor, fuel, and materials costs, respectively, during the pe-
riod 2006 to 2015. Additionally, the compound annual improvement in capital
productivity is 0.27 percent. When the target operating margin constraints are
binding at 5 percent, the reduced airline operating costs are passed along to the
traveling public as fare reductions. Consequently, an additional 83.8 billion reve-
nue passenger miles are flown. However, because of the productivity improve-
ments, airline employment decreases by over 4,000 work-years and the number of
aircraft in the fleet decreases by 100. The estimated value of these is a drop of
$5.2 billion (in 1995 dollars) from the baseline scenario. The projected U.S. mar-
ket share remains at 76 percent. This is expected to cost 32,408 work-years of
employment at U.S. airframe manufacturers.
E-9
Table E-7. RPM Growth for Technology C
Baseline Total RPM Revised Total RPM
Year (billions) (billions)
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
550.7
576.6
603.8
632.3
662.1
693.3
723.1
754.1
786.6
820.4
855.6
891.7
929.2
968.4
1,009.2
1,051.7
1,095.2
1,140.5
1,187.6
1,236.7
1,287.8
4.34%
550.7
576.6
603.8
632.3
662.1
693.3
723.1
754.1
786.6
820.4
855.6
892.9
931.8
972.3
1,014.7
1,058.8
1,104.0
1,151.1
1,200.2
1,251.4
1,304.8
4.41%
83.8
E-IO
Details of Alternative Scenarios
Table E-8. Airline Employment for Technology C
Baseline Revised
Year Employment Employment
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
438,983
455,299
472,316
490,068
508,590
527,917
545,799
564,383
583,700
603,780
624,659
645,810
667,787
690,626
714,365
739,041
764,055
790,041
817,042
845,101
874,262
3.50%
438,983
455,299
472,316
490,068
508,590
527,917
545,799
564,383
583,700
603,780
624,659
645,761
667,686
690,470
714,148
738,761
763,648
789,499
816,355
844,259
873,257
3.50%
-4,285
E-11
Table E-9. Fleet Size for Technology C
Baseline Number Revised Number
Year of Aircraft of Aircraft
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Growth Rate
Gross Change
4,179
4,297
4,419
4,545
4,676
4,812
4,932
5,055
5,183
5,315
5,451
5,587
5,727
5,872
6,021
6,175
6,329
6,488
6,652
6,821
6,995
2.61%
4,179
4,297
4,419
4,545
4,676
4,812
4,932
5,055
5,183
5,315
5,451
5,579
5,711
5,847
5,988
6,132
6,276
6,424
6,576
6,733
6,895
2.54%
-100
E-12
Appendix F
User's Guide
STARTING ACIM
The file name for the model is ACIM.xls. To run the model:
• Download ACIM.xls from the ASAC website.
• Make sure that Microsoft Excel is NOT running.
• Locate ACIM.xls in File Manager, Windows Explorer, or a similar utility.
• Double click on the file name or file icon.
The main dialog box of the ACIM will appear. This dialog box has four buttons,
that will be explained in tum.
RUN MODEL
Clicking the Run Model button displays the Run Model dialog box, which has
five buttons that will be described below.
Choose Scenario
Clicking the Choose Scenario button displays the Choose Scenario dialog box.
This dialog box contains a drop down list of scenarios of the available scenarios.The user can select a scenario from this list and then return to the Run Model
dialog box by clicking on the Return to Run Model button.
Edit Scenario
Clicking the Edit Scenario button displays the Edit Scenario dialog box. This
dialog box contains eight buttons. The first 5 buttons display dialog boxes where
the user can view and edit the scenario parameters of the chosen scenario. The
sixth button displays a dialog box where the user can enter or edit notes about the
F-1
scenario. The seventh button displays the Edit Aircraft Replacement Parameters
dialog box. The eighth button returns the user to the Run Model dialog box.
TRANSLATOR UTILITY
The first dialog box accessed from the Edit Scenario dialog box is the Edit Gross
Changes in Cost dialog box. There is a button on this box marked Translator.
Clicking this button displays the Translator dialog box, which has four buttons.
This dialog box is used to select a baseline case file and a revised case file which
have been downloaded previously to the user's system. Use the first button tochoose the baseline case file and the second button to choose the revised case file.
Use the Update Gross Changes in Cost button to calculate changes for Flight Per-
sonnel, Aircraft Fuel, and Maintenance from the selected files and display the new
figures in the Edit Gross Changes in Cost dialog box. Use the cancel button to
retum to the Edit Gross Changes in Cost dialog box without making any changes
from the Translator Utility.
EDIT AIRCRAFT REPLACEMENT PARAMETERS
Clicking the Edit Aircraft Replacement Parameters button displays the Edit Air-
craft Replacement Parameters dialog box. This dialog box has five buttons. The
first four buttons display dialog boxes where the user can view and edit additional
parameters for the chosen scenario. These parameters affect when and with what
types of equipment various aircraft will be replaced. The fifth button returns the
user to the Edit Scenario dialog box.
Save or Delete Scenario
Clicking the Save or Delete Scenario button displays the Save or Delete Scenario
dialog box. This dialog box has a drop down list from which the user can select a
scenario name. The user can delete a selected scenario by clicking the Delete
Scenario button after selecting a scenario. The user can save new edits to an old
name by selecting the name and clicking the Save Current Scenario button. The
user can save to a new name by typing a name into the edit box or editing a name
that appears in the edit box after selecting it from the list and then clicking on the
Save Current Scenario button. The only exception to this is that the baseline case
and the three technology cases that come with the model cannot be deleted or
modified under their old names. Clicking the Return to Run Model Dialog Box
button displays the Run Model dialog box without saving or deleting a scenario.
F-2
User's Guide
Solve Scenario
Clicking the Solve Scenario button displays the Solve Scenario dialog box. This
dialog box has three buttons. The first solves the scenario given the target oper-
ating margins specified by the user and calculates fare yields. The second solves
the scenario given the fare yields specified by the user and calculates operating
profit margins. The third returns the user to the Run Model dialog box without
solving the model.
Return to Main Dialog Box
Clicking the Return to Main Dialog Box button returns the user to the Main dialog
box, which is the first dialog box to appear when starting the model.
VIEW, PRINT, OR SAVE RESULTS
Clicking the View, Print, or Save Results button displays the View, Print, or Save
Results dialog box. This dialog box has four buttons.
View Results
Clicking the View Results button displays the View Results dialog box. This
dialog box contains a drop down list of the results pages that may be viewed. The
user selects one and then clicks the OK button to view it. The result page will be
displayed until the user double clicks somewhere on the result screen. At that
point, the View, Print, or Save Results dialog box will be displayed.
Print Results
Clicking the Print Results button displays the Print Results dialog box. This dia-
log box contains a drop down list of the results pages that may be printed. The
user selects one and then clicks the OK button to print it. The result will be
printed on the default printer and the View, Print, or Save Results dialog box will
be displayed.
Save Results to File
Clicking the Save Results to File button displays the Save Results dialog box.
This dialog box allows the user to select a location and file name under which all
results will be saved. This file can then be accessed later by the user. Upon
F-3
leavingthis dialog box, theView, Print, or SaveResultsdialog boxwill bedis-played.
Return to Main Dialog Box
Clicking the Return to Main Dialog Box button displays the Main dialog box.
VIEW OR PRINT DATA OR SCENARIO
Clicking the View or Print Data or Scenario button displays the View or Print
Data or Scenario dialog box. This dialog box has three buttons.
View or Print Data
Clicking the View or Print Data button displays the View or Print Data dialog
box. This dialog box contains a drop down list of the data elements that can be
viewed or printed. The user selects one from the list and then views or prints it by
clicking the appropriate button.
View or Print Scenario
Clicking the View or Print Scenario button displays the View or Print Scenario
dialog box. This dialog box contains a drop down list of the scenarios that can be
viewed or printed. The user selects one from the list and then views or prints it by
clicking the appropriate button.
Return to Main Dialog Box
Clicking the Return to Main Dialog Box button displays the Main dialog box.
EXIT MODEL
Clicking the Exit Model button exits the ACIM.
GENERAL INSTRUCTIONS
The most common set of actions when running the model is as follows:
choose a scenario,
F-4
User's Guide
• edit the scenario parameters as desired,
• save the scenario,
• solve the scenario, and
• view the results.
F-5
Form Approved
REPORT DOCUMENTATION PAGE OPMNo.0704-0188Public reportingburden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sourcesgathering, and maintaining the data needed, and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection ofinformation, including suggestions for reducing this burden, to Washington HeadquartersServices, Directoratefor Information Operations and Reports,1215 Jefferson Davis Highway,
Suite 1204, Arlington,VA 22202-4302, and to the Office of Information and RegulatoryAffairs, Office of Management and Budget,Washington, DC 20503.
1. AGENCY USE ONLY (Leave Blank) 2. REPORT DATE
April 1997
4. TITLE AND SUBTITLE
The ASAC Air Carrier Investment Model (Second Generation)
6. AUTHOR(S)
Earl R. Wingrove III, Jesse P. Johnson, Robin C. Sickles, David H. Good
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)
Logistics Management Institute
2000 Corporate Ridge
McLean, VA 22102-7805
9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)
National Aeronautics and Space Administration
Langley Research Center
Hampton, VA 23681-0001
3. REPORT TYPE AND DATES COVERED
Contractor Report
i 5. FUNDING NUMBERS
C NAS2-14361
WO 538-08-11
8. PERFORMING ORGANIZATIONREPORT NUMBER
LMI- NS602TI
10. SPONSORING/MONITORING
AGENCY REPORT NUMBER
NASA CR-201678
!11. SUPPLEMENTARY NOTES
Langley Technical Monitor. Robert Yackovetsky
Final Report
12a. DISTRIBUTION/AVAILABILITY STATEMENT
Unclassified-Unlimited
Subject Category 01
12b. DISTRIBUTION CODE
13.ABSTRACT (Maximum 200 words)
To meet its objectiveof assisting the U.S. aviation industry with the technological challenges of the future, NASA must identify research areas that have the
greatest potential for improving the operation of the air transportation system. To accomplish this, NASA is building an Aviation System Analysis Capability
(ASAC).
The ASAC differs from previous NASA modeling efforts in that the economic behavior of buyers and sellers in the air transportation and aviation industries is
central to its conception. To link the economics of flight with the technology of flight, ASAC requires a parametrically based model with extensions that link
airline operations and investments in aircraft with aircraft characteristics. This model also must provide a mechanism for incorporating air travel demand and
profitability factors into the airlines' investment decisions. Finally, the model must be flexible and capable of being incorporated into a wide-ranging suite ofeconomic and technical models that are envisioned for ASAC.
We describe a second-generation Air Carrier Investment Model that meets these requirements. The enhanced model incorporates econometric results from the
supply and demand curves faced by U.S.-scheduled passenger air carriers. It uses detailed information about their fleets in 1995 to make predictions about
future aircraft purchases. It enables analysts with the ability to project revenue passenger-miles flown, airline industry employment, airline operating profit
margins, numbers and types of aircraft in the fleet, and changes in aircraft manufacturing employment under various user-defined scenarios.
14. SUBJECT TERMS
Aeronautics, aviation system, NASA technology, air carrier investment model, air traffic supply and demand
17. SECURITY CLASSIFICATION
OF REPORT
Unclassified
18. SECURITY CLASSIFICATION
OF THIS PAGE
Unclassified
19. SECURITY CLASSIFICATIONOF ABSTRACT
15. NUMBER OFPAGES
77
16. PRICE CODE
A05
20. LIMITATION OF ABSTRACT
UL
NSN 7540-01-280-5500 Standard Form 298, (Rev. 2-89Proscn_edby ANSi Std 239-18299-01
